Sunday, November 22, 2015

"ApacheSpark"is an open-source data analytics cluster computing
framework originally developed in the AMPLab at UC Berkeley in 2009 and
became an Apache open-source project in 2010. Spark fits into the Hadoop
open-source community, building on top of the Hadoop Distributed File
System (HDFS).However, Spark is not tied to the two-stage MapReduce
paradigm, and promises performance up to 100 times faster than Hadoop
MapReduce for certain applications.
Spark provides primitives for in-memory cluster computing that allows
user programs to load data into a cluster's memory and query it
repeatedly, making it well suited to machine learning algorithms.Spark became an Apache Top-Level Project in February 2014and was previously an Apache Incubator project since June 2013. It has received code contributions from large companies that use Spark, including Yahoo! and Intelas
well as small companies and startups. By March 2014, over 150
individual developers had contributed code to Spark, representing over
30 different companies. Prior to joining Apache Incubator, versions 0.7
and earlier were licensed under the BSD License.

Apache Spark
is a fast and general cluster computing system for Big Data. Apache
Spark is more generalized system, where you can run both batch and
streaming jobs at a time. It supersedes its predecessor MapReduce in
speed by adding capabilities to process data faster in memory. It is
also more efficient on disk. It leverages in memory processing using its
basic data unit RDD (Resilient Distributed Dataset). These hold as much
dataset as possible in memory for complete lifecycle of job hence
saving on disk I/O. Some data can get spilled over disk after memory
upper limits. Spark offers development APIs for Java, Scala, Python and R languages
and an optimized engine that supports general computation graphs for
data analysis. It also supports a rich set of higher-level tools
including Spark SQL for SQL and structured data processing, MLlib for
machine learning, GraphX for graph processing, and Spark Streaming for
stream processing. Spark runs on Hadoop YARN, Apache Mesos as well as it has its own
standalone cluster manager.

Spark core API is base of Apache Spark framework, which handles job scheduling, task distribution, memory management, I/O operations and recovering from failures. Main logical data unit in spark is called RDD (Resilient Distributed Dataset), which stores data in distributed way to be processed parallel later. It lazily computes operations. Therefore, memory need not be occupied all the time, and other jobs can utilize it.

Many problems do not lend themselves to the two-step process of map and reduce . Spark can do map and reduce much faster than Hadoop can. One of the great things about Apache Spark is that it's a single
environment and you have a single API from which you can call machine
learning algorithms, or you can do graph processing or SQL. Spark's distributed data storage model, resilient distributed datasets (RDD), guarantees fault tolerance which in turn minimizes network I/O. RDDs achieve fault tolerance through a notion of lineage: if a partition of an RDD is lost, the RDD has enough information about how it was derived from other RDDs to be able to rebuild just that partition.So you don’t need to replicate data to achieve fault tolerance. In Spark MapReduce, mappers output is kept in OS buffer cache and reducers pull it to their side and write it directly to their memory, unlike Hadoop where output gets spilled to disk and read it again. Spark’s in memory cache makes it fit for machine learning algorithms where you need to use same data over and over again. Spark can run complex jobs, multiple steps data pipelines using Direct Acyclic Graph (DAGs). Spark is written in Scala and it runs on JVM (Java Virtual Machine).

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Overview :At a high level, every Spark application consists of a driver program
that runs the user’s main function and executes various parallel
operations on a cluster. The main abstraction Spark provides is a
resilient distributed dataset (RDD), which is a collection of elements
partitioned across the nodes of the cluster that can be operated on in
parallel. RDDs are created by starting with a file in the Hadoop file
system (or any other Hadoop-supported file system), or an existing Scala
collection in the driver program, and transforming it. Users may also
ask Spark to persist an RDD in memory, allowing it to be reused
efficiently across parallel operations. Finally, RDDs automatically
recover from node failures. Spark app(Driver) buils DAG()from RDD operations. DAG is split into tasks that are executed by workers as shown in the block diagram .

A second abstraction in Spark is
shared variables that can be used in parallel operations. By default,
when Spark runs a function in parallel as a set of tasks on different
nodes, it ships a copy of each variable used in the function to each
task. Sometimes, a variable needs to be shared across tasks, or between
tasks and the driver program. Spark supports two types of shared
variables: broadcast variables, which can be used to cache a value in
memory on all nodes, and accumulators, which are variables that are only
“added” to, such as counters and sums.

Spark core API: RDDs, transformations and actionsRDD (Resilient Distributed Dataset) is main logical data unit in Spark. An RDD is distributed collection of objects. Distributed means, each RDD is divided into multiple partitions. Each of these partitions can reside in memory or stored on disk of different machines in a cluster. RDDs are immutable (Read Only) data structure. You can’t change original RDD, but you can always transform it into different RDD with all changes you want. RDDs can be created by 2 ways:1. Parallelizing existing collection.2. Loading external dataset from HDFS (or any other HDFS supported file types).Creating SparkContextTo execute any operation in spark, you have to first create object of SparkContext class. A SparkContext class represents the connection to our existing Spark cluster and provides the entry point for interacting with Spark. We need to create a SparkContext instance so that we can interact with Spark and distribute our jobs. Spark provides a rich set of operators to manipulate RDDs. RDD performs 2 operations mainly, transformations and actions. Transformations:Transformations create new RDD from existing RDD like map, reduceByKey and filter we just saw. Transformations are executed on demand. That means they are computed lazily. We will see lazy evaluations more in details in next part.Lineage Graph: RDDs maintain a graph of 1 RDD getting transformed into another called lineage graph, which helps Spark to recompute any intermediate RDD in case of failures. This way spark achieves fault tolerance.

Hadoop v/s Spark Fault Tolerance

Actions return final results of RDD computations. Actions triggers execution using lineage graph to load the data into original RDD, carry out all intermediate transformations and return final results to Driver program or write it out to file system.

Spark is built using [Apache Maven](http://maven.apache.org/).To build Spark and its example programs, run:mvn -DskipTests clean package(You do not need to do this if you downloaded a pre-built package). More detailed documentation isavailable from the project site@ "http://spark.apache.or/docs/latest/building-spark.html".

2) Interactive Scala Shell: The easiest way to start using Spark is through the Scala shell:"./bin/spark-shell". Try the following command, which should return 1000:scala> sc.parallelize(1 to 1000).count()

3)Interactive Python Shell: Alternatively, if you prefer Python, you can use the Python shell: "./bin/pyspark ". And run the following command, which should also return 1000: sc.parallelize(range(1000)).count()

4)Example Programs: Spark also comes with several sample programs in the `examples` directory.To run one of them, use "./bin/run-example <class> [params]". For example:"./bin/run-example SparkPi"will run the Pi example locally. You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn-cluster" or "yarn-client" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in theexamplespackage.Forinstance: MASTER=spark://host:7077" ./bin/run-example SparkPi" Many of the example programs print usage help if no params are given.

5)Running Tests:Testingrequires [building Spark](#building-spark). Once Spark is built, testscan be run using:"./dev/run-tests". Steps to run automated tests :"https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-AutomatedTesting". 6) A Note About Hadoop Versions:Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs

Please refer to the build documentation at "http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "http://spark.apache.org/docs/latest/hadoop-third-party-distributions.html" for guidance on building a Spark application that works with a particular distribution.7) Configuration: Please refer to the Configuration guide at "http://spark.apache.org/docs/latest/configuration.html" in the online documentation for an overview on how to configure Spark.

Conclusion:Apache Spark is a fast and general engine for large-scale data processing.

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on diskWrite applications quickly in Java, Scala, Python, R

Combine SQL, streaming, and complex analytics

Spark runs on Hadoop, Mesos, standalone, or in the cloud.

It can access diverse data sources including HDFS, Cassandra, HBase, and S3.

Hadoop v/s Spark Computation Model

Spark
does not store all data in memory. But if data is in memory it makes
best use of LRU cache to process it faster. It is 100x faster while
computing data in memory and still faster on disk than Hadoop. Spark
does not have its own storage system. It relies on HDFS for that. So,
Hadoop MapReduce is still good for certain batch jobs, which does not
include much data pipelining. New technology never completely replaces
old one; they both would rather coexist.

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Spark
is not tied specifically to Hadoop. Although it does work with YARN, it
can also work well with Apache Mesos and can also read data from
Cassandra. So although Spark may become the real-time engine for Hadoop,
it can also live independent of it, with users leveraging its related
projects such as Spark SQL, Spark Streaming, and MLlib (Machine
Learning). I think this capability means that Spark will soon become
more important with Big Data developers and MapReduce will in turn
become the solution for batch processing as opposed to the core paradigm
for Hadoop. Specifically for batch use cases, MapReduce for now will be
stronger than Spark, especially for very large datasets. - See more at:
http://blog.gogrid.com/2014/07/15/mapreduce-dead/#sthash.gM4nEOrw.dpuf